CN112819595A

CN112819595A - Method and device for intelligent disposal of certificate risk

Info

Publication number: CN112819595A
Application number: CN202110041782.3A
Authority: CN
Inventors: 廖鑫
Original assignee: China Construction Bank Corp
Current assignee: China Construction Bank Corp
Priority date: 2021-01-13
Filing date: 2021-01-13
Publication date: 2021-05-18

Abstract

The invention relates to a method and a device for intelligently disposing a certificate risk, and relates to the technical field of computers. One embodiment of the method comprises: screening and integrating the pushed service flow data according to the corresponding service rules and the configuration of the parameters; calling a corresponding certificate image according to the screened and integrated service flow data; identifying the called certificate image through an identification model and extracting element information from the certificate image; comparing and judging the extracted element information with the business flow data, and determining whether a certificate risk exists according to a comparison and judgment result; and integrating the running water data and the comparison and judgment result, and then carrying out risk disposal treatment.

Description

Method and device for intelligent disposal of certificate risk

Technical Field

The invention relates to the technical field of computers, in particular to a method and a device for intelligently disposing a certificate risk.

Background

[ problem to be solved by the invention ]

1. The existing transaction paper voucher has multiple types and complex elements, and the risk of voucher element loss exists in business handling.

2. Defining multiple identification models, and extracting corresponding certificate elements aiming at different types of certificates, so that a risk monitoring scene is rapidly expanded.

3. Efficiency is improved, and the efficiency of certificate risk judgment is improved through automatic comparison of certificate images.

4. The improvement of the operation quality, the assistant of the operator judges the image information which is not automatically identified.

[ Prior Art ]

With the economic development of society and the improvement of the living standard of people, the products and the business of commercial banks are continuously increased, the business process is more complex, the bank business handling is gradually electronized, and the certificate still has an important role as the basis for recording the occurrence of the bank economic business.

The general procedure for existing credential-related risk handling is as follows:

converting the paper certificate into a certificate image through a scanner and storing the certificate image in an unstructured database, wherein the electronic image is related to business flow;

by calling the voucher image in a running way, special personnel compare the electronic image with transaction information generated during business transaction according to related business rules, and an operator visually judges whether the elements are complete and correct, so that the aim of reducing the voucher risk is fulfilled.

[ disadvantages of the prior art and objects of the present invention ]

In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art, namely that the following defects exist in the risk monitoring and disposal of the certificate in the current commercial bank:

1. the certificates used for business handling are of various types, different certificate elements are different, business scenes according to certificate comparison are different, and existing risk scenes are more and more.

2. A special operator identifies the difference between the electronic image and the transaction information through naked eyes, the operation difficulty is high, and the risk of misjudgment and misjudgment exists.

3. The method has the advantages of large traffic, long troubleshooting time, lower operation efficiency and indirectly increased personnel cost.

In order to solve the problems, the risk processing capacity of the commercial bank on the certificates is improved, automatic identification and automatic comparison are carried out by introducing an optical character identification and intelligent model, the manual comparison workload is greatly reduced, the personnel cost is reduced, the comparison speed is accelerated, in addition, an operator is converted into an auxiliary operation mode, and the certificate images which are not automatically identified are judged again, so that the overall quality and efficiency of certificate image processing are improved; the coverage of potential risks is enlarged by defining multiple risk scene models; and continuously using the statistical data to judge the identification effectiveness, verifying the accuracy of the automatic model and optimizing the quality of the model.

Disclosure of Invention

In view of this, embodiments of the present invention provide a method and an apparatus for intelligently handling credential risk, which can improve the capability of handling credential risk.

(1) To achieve the above object, according to a first aspect of embodiments of the present invention, there is provided a method for intelligent credential risk handling, including:

screening and integrating the pushed service flow data according to the corresponding service rules and the configuration of the parameters;

calling a corresponding certificate image according to the screened and integrated service flow data;

identifying the called certificate image through an identification model and extracting element information from the certificate image;

comparing and judging the extracted element information with the business flow data, and determining whether a certificate risk exists according to a comparison and judgment result; and

and integrating the running water data and the comparison and judgment result, and then carrying out risk disposal treatment.

(2) The method for intelligent disposal of certificate risk according to the (1) of the present invention, further comprising:

and according to the comparison and judgment result, carrying out statistical analysis and evaluating the accuracy of the identification model.

(3) The method for intelligent disposal of certificate risk according to (2) of the present invention, further comprising:

and adjusting the recognition model according to the result of the statistical analysis.

(4) The method for intelligent disposal of credential risk according to (2) of the present invention, wherein the statistical analysis includes at least one of: analyzing and identifying the proportion of the successful push data volume; analyzing the proportion of the missing voucher occupying the pushed data volume; analyzing and identifying the proportion of failed acquisition to the push data volume; and extracting part of identified business flow data, manually comparing again, checking the quality, checking whether misjudgment is successful or not, and further analyzing the proportion of the misjudgment in the pushed data.

(5) The method for intelligent disposal of certificate risk according to the (1) of the present invention, further comprising:

in the risk processing, when the comparison result is unidentified or inconsistent, the relevant images are retrieved, and the unidentified or inconsistent certificate images are artificially and auxiliarily judged.

(6) The method for intelligent disposal of certificate risk according to the (1) of the present invention, further comprising:

in the risk processing, when the comparison results are consistent, performing follow-up problem management on the found risk problems, issuing the found risk problems to a website for handling the business by the follow-up problem management, checking and modifying the risk problems by the website, replying the processing results, and rechecking the processing results by the management personnel.

(7) The method for intelligent disposal of credential risk according to (1) of the present invention, wherein:

different said recognition models are defined on the basis of different credentials, one credential corresponding to at least one recognition model.

(8) The method for intelligent disposal of credential risk according to (1) of the present invention, wherein:

the recognition model includes at least an element to be acquired and a location of the element.

(9) To achieve the above object, according to a second aspect of the embodiments of the present invention, there is provided an apparatus for intelligent disposal of credential risk, including:

a risk handling module, the risk handling module comprising: the risk model screening unit screens and integrates the pushed business flow data according to the configuration of the corresponding business rules and parameters; and

the module is compared to intelligence, the module is compared to intelligence includes:

the identification unit calls a corresponding certificate image according to the screened and integrated business flow data pushed by the risk handling module, identifies the called certificate image through an identification model and extracts element information from the certificate image;

the comparison unit compares the extracted element information with the service flow data;

the judging unit is used for determining whether the certificate risk exists or not according to the comparison result; and

and the integration unit integrates the running water data and the comparison and judgment results and then sends the integrated running water data and the comparison and judgment results to the risk disposal module.

(10) The apparatus for intelligent disposal of credential risk according to (9) of the present invention, wherein the risk disposal module further comprises:

and the statistical analysis unit is used for carrying out statistical analysis according to the comparison and judgment result and evaluating the accuracy of the identification model.

(11) The apparatus for intelligent disposal of credential risk according to (10) of the present invention, wherein the intelligent comparison module further comprises:

and the adjusting unit adjusts the recognition model according to the result of the statistical analysis, and the adjusting unit is an independent unit or a unit integrated in the recognition unit.

(12) The apparatus for intelligent disposal of credential risk of (10) in accordance with the present invention, wherein the statistical analysis includes at least one of: analyzing and identifying the proportion of the successful push data volume; analyzing the proportion of the missing voucher occupying the pushed data volume; analyzing and identifying the proportion of failed acquisition to the push data volume; and extracting part of identified business flow data, manually comparing again, checking the quality, checking whether misjudgment is successful or not, and further analyzing the proportion of the misjudgment in the pushed data.

(13) The apparatus for intelligent disposal of credential risk according to (9) of the present invention, wherein the risk disposal module further comprises:

the comparison result screening unit screens the comparison result returned by the intelligent comparison module; and

and a result discrimination unit that discriminates the screening result.

(14) The apparatus for intelligent disposal of credential risk of (13) of the present invention, wherein the risk disposal module further comprises:

and the manual identification unit is used for retrieving related images and carrying out manual auxiliary judgment on the unidentified certificate images when the comparison result is unidentified or inconsistent according to the judgment result of the result judgment unit.

(15) The apparatus for intelligent disposal of credential risk of (13) of the present invention, wherein the risk disposal module further comprises:

and the subsequent risk problem processing unit is used for performing subsequent problem management on the found risk problems according to the judgment result of the result judgment unit and when the comparison results are consistent, the subsequent problem management issues the found risk problems to a website of the handling service, the website checks and amends the risk problems and replies the processing result, and a manager rechecks the processing result.

(16) The apparatus for intelligent disposal of credential risk according to (9) of the present invention, wherein:

(17) The apparatus for intelligent disposal of credential risk according to (9) of the present invention, wherein:

(18) To achieve the above object, according to a third aspect of an embodiment of the present invention, there is provided a system for intelligent disposal of credential risk, including:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the method according to any one of (1) - (8) of the present invention.

(19) To achieve the above object, according to a fourth aspect of the embodiments of the present invention, there is provided a computer-readable medium on which a computer program is stored, characterized in that the program, when executed by a processor, implements the method according to any one of (1) to (8) of the present invention.

The embodiment of the invention has the following advantages or beneficial effects: because the optical character recognition and intelligent model are adopted to carry out the technical means of automatic recognition, automatic comparison, definition of various risk scene models, discrimination of recognition effectiveness by using statistical data and the like, the following technical problems in the prior art are overcome: for the voucher risk, a special operator needs to identify the difference between the electronic image and the transaction information through naked eyes, the operation difficulty is high, and the risk of misjudgment and misjudgment exists; large traffic, long investigation time, low operation efficiency, indirectly increased personnel cost and the like. Therefore, the manual comparison workload is greatly reduced, the personnel cost is reduced, the comparison speed is accelerated, and the overall quality and efficiency of voucher image processing are improved; expanding the coverage of potential risks; the accuracy of the automatic model is verified, the quality of the model is optimized, and the technical effect of the voucher risk processing capacity is further improved.

Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.

Drawings

The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:

FIG. 1 is a schematic diagram of a credential risk intelligence handling device according to the present invention;

FIG. 2 is a schematic diagram of a risk handling module of a credential risk intelligence handling device in accordance with the present invention;

FIG. 3 is a process flow diagram of a risk handling module of a credential risk intelligence handling device in accordance with the present invention;

FIG. 4 is a schematic diagram of an intelligent comparison module of the intelligent voucher risk handling device according to the present invention;

fig. 5 is a schematic processing flow diagram of the intelligent comparison module of the intelligent certificate risk handling device according to the present invention;

FIG. 6 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;

fig. 7 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.

Detailed Description

Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

Fig. 1 is a schematic diagram of a credential risk intelligent handling device according to the present invention. As shown in fig. 1, the intelligent voucher risk handling device 100 of the present invention mainly includes three modules, namely a data module 1, a risk handling module 2, and an intelligent comparison module 3. In addition, the intelligent credential risk handling device 100 of the present invention may further include a user interface module (not shown), for example, an input unit (such as a keyboard, a mouse, or any other component capable of inputting) for providing instructions or information for user input operations, for example, a display unit for presenting various processing results, model configurations, and selections to the user, which is not described herein again. The following describes the design of each module of the intelligent credential risk handling device 100.

(1) Data module

At present, banks generally build a unified business data storage system, store structured data and unstructured data generated by business handling, rely on an upstream database, and a data module is used for widely accessing various business data. The data module 1 according to the present invention may store, for example, business flow data and also image data.

(2) Risk handling module

Fig. 2 is a schematic diagram of a risk handling module of a credential risk intelligence handling device according to the present invention. Fig. 3 is a process flow diagram of a risk handling module of a credential risk smart handling device according to the present invention.

The risk handling module 2 according to the present invention is composed of a plurality of parts, such as a risk model screening unit 21, a comparison result screening unit 22, a result discrimination unit 23, a manual identification unit 24, a subsequent risk problem processing unit 25, and a statistical analysis unit 26.

< Risk model screening Unit >

The risk model filtering unit filters the data according to the corresponding business rule (S301 of fig. 3). Different risks exist in different business scenes, the risk scenes are converted into risk models through the configuration of rules and parameters, the widely accessed business data are reused, the business flow data generated by the related voucher risk scenes are screened through the risk models, and the related data are integrated and then are pushed to the intelligent comparison module 3 for next-step automatic comparison. For example, a risk model may be composed of one or more rules, which may be composed of a plurality of conditions, such as an amount >100 ten thousand dollars and an age >30 years may constitute a rule. The same rule model can adjust some parameters for different situations. The screened business flow data refers to business data screened out according to a rule under a certain risk scene (the data may have risks or problems), and may include specific data items, for example, the drawing of the periodic deposit of the deposit slip may involve periodic deposit contract data, and the periodic deposit account data is the business data under the scene.

The banking business is various and complex in content, so that the banking business is in various risk scenes. Credential risk belongs to a diverse group of risk scenarios. The risk scenarios of the present invention include, but are not limited to, credential risk scenarios. The deposit slip is a large and common type of instrument in banks, and the instruments of the present invention include, but are not limited to, deposit slips. For ease of understanding, the risk handling module 2 of the present invention will be described herein with reference to a deposit slip as an example.

In banking, for example, the drawing of a deposit on a deposit receipt is performed, and important contents such as a deposit seal, an operator seal, a name, an amount, a date of deposit, and an account number are involved in the transaction. Therefore, the risk model may be established to include some or all of the above without excluding other elements than the above elements according to the scenario of the deposit receipt being periodically deposited. For the deposit receipt periodic deposit, the larger the amount of money is, the more the integrity of the certificate elements should be controlled, for example, the data table to be accessed by the deposit receipt periodic deposit includes the accessed periodic deposit contract data and periodic deposit account data, rule conditions are set for the product, the deposit transaction amount and the due date, if the rule is set to be that the product, the deposit transaction amount and the due date are paid out by the periodic deposit receipt with the amount of money larger than five million and not due yet, the information such as the account information, the running water information, the bill number and the bill type is screened out, and the related data is integrated and then pushed to the intelligent comparison module 3 for next automatic comparison. In this scenario, the relevant data may include, for example, a savings transaction stamp, an office stamp, and a username, amount, expiration date, account number, account information, journal information, ticket number, ticket type, and so forth.

< screening means for comparison results >

The comparison result screening unit 22 screens the comparison result data received from the intelligent comparison module 3 (S302 of fig. 3). The screened result is sent to the result discrimination unit 23. The filtering may be performed according to a predetermined rule, for example, to filter only data that cannot be identified by the element, to filter only data in which the identified element and the traffic flow are not consistent, or to filter all data having any problem, or to set a combination of filtered data as necessary. The screening rules can be set by default or manually set through a user interface, so that the screening is flexible.

< result determination means >

The result discrimination unit 23 discriminates the screened result data (S303 of fig. 3). When the result data shows "inconsistency" (including the case where the image cannot be recognized), the result data is sent to the manual recognition unit 24 to perform manual recognition (S303 "inconsistency" in fig. 3). When the result data shows "agree" (S303 of fig. 3 shows "agree"), the result data is transmitted to the subsequent risk issue processing unit 25.

< Artificial identification means >

The manual identification unit 24 performs manual auxiliary determination on the returned inconsistent or unidentified certificate images (S305 in fig. 3), and the data module 1 searches for the relevant images (for example, according to serial numbers) to perform manual comparison, so as to improve the overall identification quality. Thereafter, the manual recognition unit 24 transmits the processed result to the subsequent risk issue processing unit 25.

< follow-up Risk problem processing Unit >

The subsequent risk problem processing unit 25 performs subsequent problem management on the found problem (S304 in fig. 3), the manager issues the found problem to a website that performs the service, the website replies after checking and modifying the problem, the manager rechecks the related problem, and the risk problem is managed from finding to modifying to checking the whole process.

< statistical analysis Unit >

The risk processing module 2 also receives the returned result of the intelligent comparison module 3, and the statistical analysis unit 26 performs statistical analysis on the comparison result data to evaluate the accuracy of the identification model. The statistical analysis may include one or more of, but is not limited to, the following: analyzing and identifying the proportion of successful push data volume occupation, analyzing the proportion of missing voucher data volume occupation, analyzing and identifying the proportion of failed acquisition data volume occupation, extracting part of identified business flow, manually comparing again, performing quality inspection, checking whether the misjudgment is successful or not, and further analyzing the proportion of the misjudgment data volume occupation. For analysis, corresponding predetermined thresholds may be set for the ratio of successfully identified push data amount, the ratio of missing voucher push data amount, the ratio of failed identified collection push data amount, and the ratio of erroneous determination, respectively. For example, when the ratio of the amount of the push data occupied by the identification success is smaller than or equal to a predetermined threshold, or when the ratio of the amount of the push data occupied by the identification loss is larger than or equal to a predetermined threshold, or the ratio of the amount of the push data occupied by the acquisition failure is larger than or equal to a predetermined threshold, the identification model needs to be adjusted. The statistical analysis can be performed on batch data pushed once, batch data pushed several times in a time period, or result analysis on data pushed by a specific type of risk model. The statistical analysis unit 26 sends the statistical analysis result to the intelligent comparison module 3 for adjustment of the recognition model.

(2) Intelligent comparison module

Fig. 4 is a schematic diagram of an intelligent comparison module of a credential risk intelligent handling device according to the present invention. Fig. 5 is a schematic processing flow diagram of the intelligent comparison module of the intelligent certificate risk handling apparatus according to the present invention.

The intelligent comparison module 3 receives the business pipeline data pushed by the risk handling module 2, calls the corresponding voucher image (for example, according to the pipeline number), identifies the image through the identification model, compares the extracted element information with the generated business data for judgment, and sends the comparison result to the risk handling module 2.

The intelligent comparison module 3 according to the invention comprises: the identification unit 31 is configured to receive the service flow data pushed by the risk handling module 2, call a voucher image corresponding to the service flow data, and identify the image through an identification model; the comparison unit 32 is used for comparing the information identified by the identification unit with the service flow data; a determining unit 33, configured to determine a comparison result, where the determination comparison result may include an element information identification result (complete identification, partial identification, non-identification, and the like) and a comparison result (coincidence, missing, and the like); and an integrating unit 34 configured to integrate the identification result data and send the integrated identification result data to the risk handling module 2.

First, in S501 of fig. 5, the identifying unit 31 receives the traffic pipelining data pushed by the risk handling module 2. For example, the business flow data of a personal periodical transaction may include part or all of the data of deposit transaction amount, currency code, flow number, customer account number, customer name, bill category code, etc., and is not limited to the listed data, but may include other related data.

Then, the recognition unit 31 calls a voucher image corresponding to the business pipeline data, and recognizes the image through a recognition model. The identification unit may call the corresponding credential image according to the serial number, for example. Different voucher images correspond to different voucher templates. The recognition unit 31 constructs extraction elements corresponding to the recognition models for different credential templates. For example, for a personal settlement duplicate voucher, such as a deposit slip, the voucher number, account number, username, amount, etc. can be identified; for public settlement duplicate and empty vouchers such as transfer checks, common checks, cash checks, real-time general vouchers and the like, voucher numbers, capital and lower amount, ticket issuing dates and the like can be identified; for the public settlement non-duplicate and non-null voucher, the information such as a payment account number, capital and small amount, transaction date and the like can be identified. The recognition unit 31 recognizes key contents included in the voucher image, and extracts element information therefrom.

The intelligent comparison module 3 can define various identification models according to different certificate templates corresponding to the certificate images, set parameter information aiming at different certificate images and certificate element information, and flexibly define elements to be collected and positions of the elements. For example, the following table is stored in the memory, and different recognition models are set according to the credential type (credential template). The elements in the recognition model can be increased or decreased as required, and each element corresponds to an element position. The same type of voucher has different formats due to different regions or different time, and may have different elements and element positions, so that the same type of voucher may also have multiple corresponding recognition models. Such as illustrated by the personal settlement flyover voucher a for the deposit slip and the personal settlement flyover voucher B for the deposit slip in the list. As another example, indicia such as date or region of application of the credential layout may optionally be added to facilitate accurate selection of the recognition model.

Next, in step S502 of fig. 5, the comparison unit 32 compares the extracted factor information with the generated service data. In step S503 of fig. 5, the discrimination unit 33 discriminates the result by comparison. Then, in step S504 of fig. 5, the integration unit 34 integrates the recognition result and pushes the recognition result to the risk handling module 2, and the risk handling module 2 handles the discovered or unidentified credential problem.

The intelligent comparison module 3 may also receive statistical data of the risk handling module 2, including success rate of the designated credential identification, false identification rate of the sampling, and the like. Statistics such as, but not limited to, the following items:

and aiming at the identification model with lower identification success rate, analyzing the accuracy of the comparison of the certificate elements, analyzing the elements with higher comparison failure rate in the certificate extraction elements, adjusting parameters and continuously optimizing and adjusting the identification model. For example, if the recognition failure rate of only individual elements is high, the recognition success rate can be improved by adjusting the recognition positions of the elements; if the recognition success rate of the recognition model is extremely low, the recognition model of the certificate can be adjusted from the current model to another recognition model of the certificate, so that the recognition model is more accurate. This function of the intelligent comparison module 3 may be performed by a separate recognition model analysis and adjustment unit (not shown) or may be integrated into the recognition unit 31.

FIG. 6 illustrates an apparatus exemplary system architecture 600 for intelligent disposal of credential risk to which the method of intelligent disposal of credential risk of embodiments of the present invention may be applied.

As shown in fig. 6, the system architecture 600 may include

terminal devices

601, 602, 603, a network 604, and a server 605. The network 604 serves to provide a medium for communication links between the

terminal devices

601, 602, 603 and the server 605. Network 604 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.

A user may use the

terminal devices

601, 602, 603 to interact with the server 605 via the network 604 to receive or send messages or the like. The

terminal devices

601, 602, 603 may have installed thereon various communication client applications, such as a web browser application, an instant messaging tool, a mailbox client, etc. (by way of example only).

The

terminal devices

601, 602, 603 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 605 may be a server providing various services, such as a background management server (for example only) providing support for users utilizing the

terminal devices

601, 602, 603. The background management server can analyze and process the received data and feed back the processing result to the terminal equipment.

It should be noted that the method for intelligently handling credential risk provided by the embodiment of the present invention is generally executed by the server 605, and accordingly, the apparatus for intelligently handling credential risk is generally disposed in the server 605.

It should be understood that the number of terminal devices, networks, and servers in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Referring now to FIG. 7, shown is a block diagram of a computer system 700 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.

As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM703, various programs and data necessary for the operation of the system 700 are also stored. The CPU 701, the ROM 702, and the RAM703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.

In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 701.

It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units (or "modules") described in the embodiments of the present invention may be implemented by software or hardware. The described units (or "modules") may also be provided in a processor, and may be described as, for example: a processor includes a risk handling module and an intelligent comparison module. Where the names of these modules do not in some cases constitute a limitation on the module itself, for example, a "risk handling module" may also be described as a "risk handling unit".

As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise:

The present invention is described in detail above with reference to the accompanying drawings. It will be apparent to those skilled in the art that the present invention has the following innovations:

1. the invention optimizes the risk disposal of the voucher by adopting an automatic identification and automatic comparison model, and improves the quality and efficiency of operation.

2. And defining a risk identification model and expanding the coverage range of a risk scene.

In the aspect of avoiding the current certificate risks, a commercial bank mainly compares the certificate image information manually, the efficiency is low, the accuracy is not high enough, the labor cost is increased gradually, the manual identification capability is weak, and the covered risk scene is small. And secondly, flexibly configuring an identification model aiming at different risk scenes to help a user to quickly find and avoid risks.

Although the invention has been described with reference to bank credential risk, it will be apparent that the invention may be used in other industries or fields where there is a risk of a credential.

The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method of intelligent disposal of credential risk, comprising:

2. The method for intelligent disposal of credential risk as recited in claim 1, further comprising:

3. The method of intelligent credential risk handling as recited in claim 2, further comprising:

4. A method of intelligent credential risk handling as defined in claim 2, wherein the statistical analysis includes at least one of: analyzing and identifying the proportion of the successful push data volume; analyzing the proportion of the missing voucher occupying the pushed data volume; analyzing and identifying the proportion of failed acquisition to the push data volume; and extracting part of identified business flow data, manually comparing again, checking the quality, checking whether misjudgment is successful or not, and further analyzing the proportion of the misjudgment in the pushed data.

5. The method for intelligent disposal of credential risk as recited in claim 1, further comprising:

6. The method for intelligent disposal of credential risk as recited in claim 1, further comprising:

7. The method for intelligent disposal of credential risk as defined in claim 1, wherein:

8. The method for intelligent disposal of credential risk as defined in claim 1, wherein:

9. An apparatus for intelligent handling of credential risk, comprising:

10. The intelligent credential risk handling device according to claim 9, wherein the risk handling module further comprises:

11. The apparatus for intelligent credential risk handling according to claim 10, wherein the intelligent comparison module further comprises:

12. An apparatus for intelligent credential risk handling as defined in claim 10 wherein the statistical analysis includes at least one of: analyzing and identifying the proportion of the successful push data volume; analyzing the proportion of the missing voucher occupying the pushed data volume; analyzing and identifying the proportion of failed acquisition to the push data volume; and extracting part of identified business flow data, manually comparing again, checking the quality, checking whether misjudgment is successful or not, and further analyzing the proportion of the misjudgment in the pushed data.

13. The intelligent credential risk handling device according to claim 9, wherein the risk handling module further comprises:

and a result discrimination unit that discriminates the screening result.

14. The apparatus for intelligent disposal of credential risk of claim 13, wherein the risk handling module further comprises:

15. The apparatus for intelligent disposal of credential risk of claim 13, wherein the risk handling module further comprises:

16. The apparatus for intelligent credential risk handling as recited in claim 9, wherein:

17. The apparatus for intelligent credential risk handling as recited in claim 9, wherein:

18. A system for intelligent disposal of credential risk, comprising:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-8.

19. A computer-readable medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method according to any one of claims 1-8.